USGS ScienceSearch

USGS · 70012605

Range charts and no-space graphs

Abstract

No-space graphs present one solution to the familiar problem: given data on the occurrence of fossil taxa in separate, well-sampled sections, determine a range chart; that is, a reasonable working hypothesis of the total range in the area in question of each taxon studied. The solution presented here treats only the relative sequence of biostratigraphic events (first and last occurrences of taxa) and does not attempt to determine an amount of spacing between events. Relative to a hypothesized sequence, observed events in any section may be in-place or out-of-place. Out-of-place events may indicate (1) the event in question reflects a taxon that did not fill its entire range (unfilled-range event), or (2) the event in question indicates a need for the revision of the hypothesized sequence. A graph of relative position only (no-space graph) can be used to facilitate the recognition of in-place and out-of-place events by presenting a visual comparison of the observations from each section with the hypothesized sequence. The geometry of the graph as constructed here is such that in-place events will lie along a line series and out-of-place events will lie above or below it. First-occurrence events below the line series and last-occurrence events above the line series indicate unfilled ranges. First-occurrence events above the line series and last-occurrence events below the line series indicate a need for the revision of the hypothesis. Knowing this, the stratigrapher considers alternative positionings of the line series as alternative range hypotheses and seeks the line series that best fits his geologic and paleontologic judgment. No-space graphs are used to revise an initial hypothesis until a final hypothesis is reached. In this final hypothesis every event is found in-place in at least one section, and all events in all sections may be interpreted to represent in-place events or unfilled-range events. No event may indicate a need for further range revision. The application of the no-space graph method requires the assumption of lack of reworking and the assumption that taxa that are present in a single horizon indicate taxa whose ranges overlap. When applied to hypothetical and actual data, the no-space graph technique produces geologically reasonable range charts that compare favorably with results produced by other methods.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Lucy E. Edwards. 1978. Range charts and no-space graphs. https://doi.org/10.1016/0098-3004(78)90057-2

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

3D semantic mapping of surface geological features

Semantic mapping in 3D is fundamental to a wide range of geoscientific studies and applications, including geomorphology, hazard assessment, and environmental monitoring. However, automatically segmenting geological features from large-scale photogrammetric datasets remains a significant challenge. We present a methodology to address this gap. Using overlapping images collected over environments of interest, Structure-from-Motion (SfM) produces georeferenced point clouds and estimates camera poses. Existing large vision models, such as Segment Anything Model, segment objects in the images, generating pixel-segmentation associations. To produce pixel-point associations, we project the points back onto the camera image planes. As objects are independently segmented across multiple images with different perspectives, we develop a segmentation mosaicking algorithm to build probabilistic point-segmentation associations that combines the pixel-segmentation associations and pixel-point associations. Our methodology is validated using both synthetic data generated by Kubric and real-world UAV-SfM data. The implementation is designed to be compatible with existing SfM software, including Agisoft and OpenDroneMap, for photogrammetry mapping in geoscience studies. As a case study, we apply our method to the semantic mapping of precariously balanced rocks (PBRs), which provide upper-bound constraints on historical ground motion shaking intensity. To support object-level identification of PBRs, we additionally integrated Grounding DINO, enabling text-prompted segmentation of features of interest within UAV imagery. This case study demonstrates the effectiveness of our method in generating a 3D semantic map of PBRs, enabling spatial distribution of PBR fragility for earthquake hazard analysis.

Computers & Geosciences

gTOOLS, an open-source MATLAB program for processing high precision, relative gravity data for time-lapse gravity monitoring

gTOOLS is an open-source software for the processing of relative gravity data. gTOOLS is available in MATLAB and as a compiled executable to be run under the free MATLAB Runtime Compiler. The software has been designed for time-lapse (temporal) gravity monitoring. Although programmed to read the Scintrex CG-5 and CG-6 gravimeters output data files, it can be easily modified to read data files from other gravimeters. The software binds together single-task processing modules within a very simple user interface that is based on one text file. Gravity processing involves three modules: (a) gravimeter calibration; (b) automatic processing of gravity data to find adjusted gravity differences; and (c) post processing of results. Each module is optional and runs independently from the others. Data processing includes (a) averaging out the measurements noise, and correction for solid Earth tides, and ocean loading, and residual instrumental drift, and (b) calculate the residual instrumental drift and gravity differences between the base station and monitoring sites, and their uncertainties, by a weighted least square analysis of the gravity data. The software allows the automatic processing of a gravity campaign spanning multiple days in a single run. The software is tested on gravity data from 2015 eruption at Cotopaxi volcano, Ecuador.

Computers & Geosciences

MTH5: An archive and exchangeable data format for magnetotelluric time series data

Magnetotellurics (MT) is a passive electromagnetic geophysical method that measures variations in subsurface electrical resistivity. MT data are collected in the time domain and processed in the frequency domain to produce estimates of a transfer function representing the Earth’s electrical structure. Unfortunately, the MT community lacks metadata and data standards for time series data. As the community grows and findability, accessibility, interoperability, and reuse of digital assets (FAIR) data principles are enforced by government and funding agencies, a standard is needed for time series data. Presented here is a hierarchical data format (MTH5) that is logically formatted to how MT data are collected. Open-source Python packages are also described to read, write, and manipulate MTH5 files. These include a package to deal with metadata ( mt_metadata ) based on standards developed by the Working Group for Magnetotelluric Data Handling and Software assembled by the Incorporated Research Institutions for Seismology (IRIS), and mth5 : a package to interact with MTH5 files that uses mt_metadata . Example code and workflows are presented.

Computers & Geosciences